摘要
针对图像处理中的模糊边缘检测问题,提出一种免疫进化模糊聚类算法.该算法在传统遗传算法全局随机搜索的基础上,借鉴了生物免疫机制中抗体的多样性保持策略,改善了遗传算法的群体多样性,具有更好的全局搜索能力.实验结果表明,该算法不仅具有很强的模糊边缘和微细边缘检测能力,而且可以减弱基于遗传算法的模糊聚类算法在遗传后期的波动现象.
A novel fuzzy clustering method based on immune evolutionary algorithm (IEFCM) was presented to solve fuzzy edge detection problems in image processing. Based on the global stochastic searching method of classic genetic algorithm (GA), and using the diversity preservation strategy of antibodies in biology immunity mechanism, the method greatly improves the colony diversity of GA and has better global searching capability. The experimental results show that the method can not only correctly detect the fuzzy edge and exiguous edge, but also evidently restrain the degenerating phenomenon during the evolutionary process.
出处
《西安交通大学学报》
EI
CAS
CSCD
北大核心
2004年第3期274-277,312,共5页
Journal of Xi'an Jiaotong University
基金
陕西省自然科学基金资助项目 (2 0 0 1x1 7)
陕西省机械制造装备重点实验室资助项目 (0 3JF0 6)
关键词
免疫进化算法
模糊聚类算法
边缘检测
Evolutionary algorithms
Fuzzy sets
Genetic algorithms
Image processing
Immunization
